Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …

The missing data problem is inevitable when collecting traffic data from intelligent
transportation systems. Previous studies have shown the advantages of tensor completion …

Abstract The Internet of Vehicles (IoV) can obtain traffic information through a large number
of data collected by sensors. However, the lack of data, abnormal data, and other low-quality …

Recent developments in sensing and monitoring techniques have led to the generation of
high-dimensional data in the field of civil engineering. High-dimensional data analytics …

In this article, a delay-compensation-based state estimation (DCBSE) method is given for a
class of discrete time-varying complex networks (DTVCNs) subject to network-induced …

In reality, readings of sensors on highways are usually missing at various unexpected
moments due to some sensor or communication errors. These missing values do not only …

Sparsity and missing data problems are very common in spatiotemporal traffic data collected
from various sensing systems. Making accurate imputation is critical to many applications in …

Spatiotemporal traffic data, which represent multidimensional time series on considering
different spatial locations, are ubiquitous in real-world transportation systems. However, the …

With the rapid development of sensor technologies, time series data collected by multiple
and spatially distributed sensors have been widely used in different research fields …

Rapid advances in sensor, wireless communication, cloud computing and data science
have brought unprecedented amount of data to assist transportation engineers and …